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Image processing articles within Scientific Reports

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Editorial article, editorial: current trends in image processing and pattern recognition.

• PAMI Research Lab, Computer Science, University of South Dakota, Vermillion, SD, United States

Editorial on the Research Topic Current Trends in Image Processing and Pattern Recognition

Technological advancements in computing multiple opportunities in a wide variety of fields that range from document analysis ( Santosh, 2018 ), biomedical and healthcare informatics ( Santosh et al., 2019 ; Santosh et al., 2021 ; Santosh and Gaur, 2021 ; Santosh and Joshi, 2021 ), and biometrics to intelligent language processing. These applications primarily leverage AI tools and/or techniques, where topics such as image processing, signal and pattern recognition, machine learning and computer vision are considered.

With this theme, we opened a call for papers on Current Trends in Image Processing & Pattern Recognition that exactly followed third International Conference on Recent Trends in Image Processing & Pattern Recognition (RTIP2R), 2020 (URL: http://rtip2r-conference.org ). Our call was not limited to RTIP2R 2020, it was open to all. Altogether, 12 papers were submitted and seven of them were accepted for publication.

In Deshpande et al. , authors addressed the use of global fingerprint features (e.g., ridge flow, frequency, and other interest/key points) for matching. With Convolution Neural Network (CNN) matching model, which they called “Combination of Nearest-Neighbor Arrangement Indexing (CNNAI),” on datasets: FVC2004 and NIST SD27, their highest rank-I identification rate of 84.5% was achieved. Authors claimed that their results can be compared with the state-of-the-art algorithms and their approach was robust to rotation and scale. Similarly, in Deshpande et al. , using the exact same datasets, exact same set of authors addressed the importance of minutiae extraction and matching by taking into low quality latent fingerprint images. Their minutiae extraction technique showed remarkable improvement in their results. As claimed by the authors, their results were comparable to state-of-the-art systems.

In Gornale et al. , authors extracted distinguishing features that were geometrically distorted or transformed by taking Hu’s Invariant Moments into account. With this, authors focused on early detection and gradation of Knee Osteoarthritis, and they claimed that their results were validated by ortho surgeons and rheumatologists.

In Tamilmathi and Chithra , authors introduced a new deep learned quantization-based coding for 3D airborne LiDAR point cloud image. In their experimental results, authors showed that their model compressed an image into constant 16-bits of data and decompressed with approximately 160 dB of PSNR value, 174.46 s execution time with 0.6 s execution speed per instruction. Authors claimed that their method can be compared with previous algorithms/techniques in case we consider the following factors: space and time.

In Tamilmathi and Chithra , authors carefully inspected possible signs of plant leaf diseases. They employed the concept of feature learning and observed the correlation and/or similarity between symptoms that are related to diseases, so their disease identification is possible.

In Das Chagas Silva Araujo et al. , authors proposed a benchmark environment to compare multiple algorithms when one needs to deal with depth reconstruction from two-event based sensors. In their evaluation, a stereo matching algorithm was implemented, and multiple experiments were done with multiple camera settings as well as parameters. Authors claimed that this work could be considered as a benchmark when we consider robust evaluation of the multitude of new techniques under the scope of event-based stereo vision.

In Steffen et al. ; Gornale et al. , authors employed handwritten signature to better understand the behavioral biometric trait for document authentication/verification, such letters, contracts, and wills. They used handcrafter features such as LBP and HOG to extract features from 4,790 signatures so shallow learning can efficiently be applied. Using k-NN, decision tree and support vector machine classifiers, they reported promising performance.

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The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Santosh, KC, Antani, S., Guru, D. S., and Dey, N. (2019). Medical Imaging Artificial Intelligence, Image Recognition, and Machine Learning Techniques . United States: CRC Press . ISBN: 9780429029417. doi:10.1201/9780429029417

CrossRef Full Text | Google Scholar

Santosh, KC, Das, N., and Ghosh, S. (2021). Deep Learning Models for Medical Imaging, Primers in Biomedical Imaging Devices and Systems . United States: Elsevier . eBook ISBN: 9780128236505.

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Santosh, KC (2018). Document Image Analysis - Current Trends and Challenges in Graphics Recognition . United States: Springer . ISBN 978-981-13-2338-6. doi:10.1007/978-981-13-2339-3

Santosh, KC, and Gaur, L. (2021). Artificial Intelligence and Machine Learning in Public Healthcare: Opportunities and Societal Impact . Spain: SpringerBriefs in Computational Intelligence Series . ISBN: 978-981-16-6768-8. doi:10.1007/978-981-16-6768-8

Santosh, KC, and Joshi, A. (2021). COVID-19: Prediction, Decision-Making, and its Impacts, Book Series in Lecture Notes on Data Engineering and Communications Technologies . United States: Springer Nature . ISBN: 978-981-15-9682-7. doi:10.1007/978-981-15-9682-7

Keywords: artificial intelligence, computer vision, machine learning, image processing, signal processing, pattern recocgnition

Citation: Santosh KC (2021) Editorial: Current Trends in Image Processing and Pattern Recognition. Front. Robot. AI 8:785075. doi: 10.3389/frobt.2021.785075

Received: 28 September 2021; Accepted: 06 October 2021; Published: 09 December 2021.

Edited and reviewed by:

Copyright © 2021 Santosh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: KC Santosh, [email protected]

This article is part of the Research Topic

Current Trends in Image Processing and Pattern Recognition

Recent trends in image processing and pattern recognition

• Guest Editorial
• Published: 27 October 2020
• Volume 79 , pages 34697–34699, ( 2020 )

Cite this article

• K. C. Santosh 1 &
• Sameer K. Antani 2  

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The Call for Papers of the special issue was initially sent out to the participants of the 2018 conference (2nd International Conference on Recent Trends in Image Processing and Pattern Recognition). To attract high quality research articles, we also accepted papers for review from outside the conference event. Of 123 submissions, 22 papers were accepted. The acceptance rate, therefore, is just under 18%.

In “Multilevel Polygonal Descriptor Matching Defined by Combining Discrete Lines and Force Histogram Concepts,” authors presented a new method to describe shapes from a set of polygonal curves using a relational descriptor. In their study, relational descriptor is the main idea of the paper.

In “An Asymmetric Cryptosystem based on the Random Weighted Singular Value Decomposition and Fractional Hartley Domain,” authors proposed an encryption system for double random phase encoding based on random weighted singular value decomposition and fractional Hartley transform domain. Authors claimed that the proposed cryptosystem is efficiently compared with singular value decomposition and truncated singular value decomposition.

In “Classification of Complex Environments using Pixel Level Fusion of Satellite Data,” authors analyzed composite land features by fusing two original hyperspectral and multispectral datasets. In their study, the fusion image technique was found to be superior to the single original image.

In “Image Dehazing using Window-based Integrated Means Filter,” authors reported that the proposed technique outperforms the state-of-the-arts in single image dehazing approaches.

In “Research on Fundus Image Registration and Fusion Method based on Nonsubsampled Contourlet and Adaptive Pulse Coupled Neural Network,” authors presented a registration and fusion method of fluorescein fundus angiography image and color fundus image that combines Nonsubsampled Contourlet (NSCT) and adaptive Pulse Coupled Neural Network (PCNN). Authors claimed that the image fusion provides an effective reference for the clinical diagnosis of fundus diseases.

In “Super Resolution of Single Depth Image based on Multi-dictionary Learning with Edge Feature Regularization,” authors focused on super resolution based on multi-dictionary learning with edge regularization model. With this, the reconstructed depth images were found to be superior with respect to the state-of-art methods.

In “A Universal Foreground Segmentation Technique using Deep Neural Network,” authors presented an idea of optical-flow details to make use of temporal information in deep neural network.

In “Removal of ‘Salt & Pepper’ Noise from Color Images using Adaptive Fuzzy Technique based on Histogram Estimation,” authors focused on the use of processing window that is based on local noise densities using fuzzy based criterion.

In “Image Retrieval by Integrating Global Correlation of Color and Intensity Histograms with Local Texture Features,” authors integrated color, intensity histograms with local state-of-the-art texture features to perform content-based image retrieval.

In “Image-based Features for Speech Signal Classification,” authors analyzed speech signal with the help of image features. Authors used the idea of computer-based image features for speech analysis.

In “ Ensembling Handcrafted Features with Deep Features: An Analytical Study for Classification of Routine Colon Cancer Histopathological Nuclei Images,” authors studied deep learning models to analyze medical histopathology: classification, segmentation, and detection.

In “Non-destructive and Cost-effective 3D Plant Growth Monitoring System in Outdoor Conditions,” authors monitored plant growth precisely with the use of mobile phone.

In “Fusion based Feature Reinforcement Component for Remote Sensing Image Object Detection,” authors employed reinforcement component (FB-FRC) to improve image classification, where two fusion strategies are proposed: a hard-fusion strategy through artificially set rules; and a soft fusion strategy by learning the fusion parameters.

In “An Improved Cuckoo Search Algorithm for Multi-level Gray-scale Image Thresholding,” authors employed computationally efficient cuckoo search algorithm.

In “Image Fuzzy Enhancement Algorithm based on Contourlet Transform Domain,” authors focused on enhancing globally the texture and edge of the image.

In “Pixel Encoding for Unconstrained Face Detection,” authors employed handcrafted and visual features to detect human faces. Authors claimed an improvement when handcrafted and visual features are combined.

In “Data Augmentation for Handwritten Digit Recognition using Generative Adversarial Networks (GAN),” authors focused on the technique that does not require prior knowledge of the possible variabilities that exist across examples to create novel artificial examples.

In “Akin-based Orthogonal Space (AOS): A Subspace Learning Method for Face Recognition,” authors reported the use of subspace learning method is efficient for human face recognition.

In “A Kernel Machine for Hidden Object-Ranking Problems (HORPs),” authors proposed a kernel machine that allows retaining item-related ordinal information while avoiding emphasizing class-related information.

In “Verification of Genuine and Forged Offline Signatures using Siamese Neural Network (SNN),” authors reported one shot learning in SNN for signature verification.

In “Super-Resolution Quality Criterion (SRQC): A Super-Resolution Image Quality Assessment Metric,” authors reported the importance of SRQC in assessing image quality. In their experiments, authors found that the SRQC is more competent in modeling the features from curvelet transform that quantifies the quality score of the super-resolved image and it outperforms the formerly reported image quality assessment metrics.

In “Ensemble based Technique for the Assessment of Fetal Health using Cardiotocograph – A Case Study with Standard Feature Reduction Techniques,” authors reported the use of state-of-the-art feature reduction techniques to assess fetal health using cardiotocograph.

Within the scope of image processing pattern recognition, this special issue includes multiple applications domains, such as satellite imaging, biometrics, speech processing, medical imaging, and healthcare.

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Santosh, K.C., Antani, S.K. Recent trends in image processing and pattern recognition. Multimed Tools Appl 79 , 34697–34699 (2020). https://doi.org/10.1007/s11042-020-10093-3

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Published : 27 October 2020

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DOI : https://doi.org/10.1007/s11042-020-10093-3

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